Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients. Radiother Oncol 2023 Aug;185:109723
Date
05/28/2023Pubmed ID
37244355Pubmed Central ID
PMC10524941DOI
10.1016/j.radonc.2023.109723Scopus ID
2-s2.0-85160710340 (requires institutional sign-in at Scopus site) 1 CitationAbstract
BACKGROUND AND PURPOSE: Late radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria.
MATERIALS AND METHODS: We applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria.
RESULTS: The PRFR method achieved significantly better predictive performance compared to other alternative methods (all p < 0.05). The odds ratio between the high and low risk groups, each of which consisted of 1/3 of samples in the validation set, was 2.87 (p = 0.029), implying a clinically useful level of discrimination. Bioinformatics analysis identified six key proteins encoded by CTNND2, GSK3B, KCNQ2, NEDD4L, PRKAA1, and TXNL1 genes as well as four statistically significant biological process networks previously shown to be associated with the bladder and urinary tract.
CONCLUSION: The risk of hematuria is significantly dependent on common genetic variants. The PRFR algorithm resulted in a stratification of prostate cancer patients at differential risk levels of post-radiotherapy hematuria. Bioinformatics analysis identified important biological processes involved in radiation-induced hematuria.
Author List
Oh JH, Lee S, Thor M, Rosenstein BS, Tannenbaum A, Kerns S, Deasy JOAuthor
Sarah L. Kerns PhD Associate Professor in the Radiation Oncology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Genome-Wide Association StudyGerm Cells
Hematuria
Humans
Male
Polymorphism, Single Nucleotide
Prostatic Neoplasms
Urinary Bladder